PROJECT SUMMARY Brain metastases (BMs) are a life-threatening neurological disease, but current treatment regimens cannot manage multiple (>4) BMs (mBMs) without causing strong adverse effects. Stereotactic radiosurgery (SRS), utilizing potent dose to irradiate BMs and quick dose falloff to spare nearby tissues, has proven to be an effective treatment regimen for limited-number and small-size BMs. However, SRS could not avoid high toxic dose when BMs are multiple, clustered, or adjacent to critical organs. To safe and effectively treat mBMs with SRS requires addressing these urgent needs: 1) to identify the maximum tolerable SRS dose; 2) to study neurocognitive decline and design strategies to preserve patients? post-treatment quality of life; and 3) to develop and implement high-quality streamlined mBMs SRS treatment and follow-up care. To address mBMs SRS management needs, we aim to develop and implement an artificial intelligence (AI)- driven treatment planning system (TPS) and conduct a therapeutic intervention clinical trial, both dedicated to improve mBMs SRS treatment quality and efficiency. The AI-driven TPS, namely AimBMs, will have three AI- based computational modules, including AI-Segtor for automatic segmentation, AI-Predictor for treatment outcome prediction and AI-Planner for spatiotemporal distributed SRS plan optimization. AimBMs is initially developed based on retrospective data and facilitate the mBMs distributed SRS prospective phase I/II clinical trials, while the clinical trial will provide critical clinical knowledge and evidence as feedback to improve AimBMs performance. The ultimate goal of the project is to translate the AimBMs to routine clinical practice to improve mBMs SRS treatment quality, patients? post-treatment QoL, and clinical facility workflow. In response to PAR-18-560, we have formed a multidisciplinary collaboration between radiation oncologists and medical physicists to develop a novel AI-driven distributed SRS technology and conduct a cancer-targeted therapeutic intervention for managing mBMs. The project?s innovations include: 1) novel SRS treatment planning technological capability enabled by AI-based auto-segmentation, treatment outcome prediction, and spatiotemporal plan optimization; 2) novel AI learning capability to improve developed AI tools? performance through the coherent clinical trial. The technology development will support the therapeutic intervention clinical trial, while the clinical trial is designated to improve the developed system performance. This seamlessly integrated development mode ensures the developed system is clinically practical. Upon completion, our newly developed AimBMs will lay a solid foundation for mBMs SRS management and benefit a wide population of patients with BMs. Moreover, the AI-based treatment planning and treatment delivery infrastructure built for mBMs SRS can be transferred to other tumor sites to generate an even broader clinical impact.